Saturday, November 12, 2016

We have released PyPy2.7 v5.6 [0], about two months after PyPy2.7 v5.4. This new PyPy2.7 release includes the upstream stdlib version 2.7.12.

We continue to make incremental improvements to our C-API compatibility layer (cpyext). We pass all but 12 of the over-6000 tests in the upstream NumPy test suite, and have begun examining what it would take to support Pandas and PyQt.

Work proceeds at a good pace on the PyPy3.5 version due to a grant from the Mozilla Foundation, and some of those changes have been backported to PyPy2.7 where relevant.

The PowerPC and s390x backend have been enhanced with the capability to use SIMD instructions for micronumpy loops.

We changed timeitto now report average +/- standard deviation, which is better than the misleading minimum value reported in CPython.

We now support building PyPy with OpenSSL 1.1 in our built-in _ssl module, as well as maintaining support for previous versions.

CFFI has been updated to 1.9, improving an already great package for interfacing with C.

As always, this release fixed many issues and bugs raised by the growing community of PyPy users. We strongly recommend updating. You can download the PyPy2.7 v5.6 release here:

Downstream packagers have been hard at work. The Debian package is already available, and the portable PyPy versions are also ready, for those who wish to run PyPy on other Linux distributions like RHEL/Centos 5.

We would like to thank our donors for the continued support of the PyPy project.

We would also like to thank our contributors and encourage new people to join the project. PyPy has many layers and we need help with all of them: PyPy and RPython documentation improvements, tweaking popular modules to run on pypy, or general help with making RPython’s JIT even better.

What is PyPy?

PyPy is a very compliant Python interpreter, almost a drop-in replacement for CPython 2.7. It’s fast (PyPy and CPython 2.7.x performance comparison) due to its integrated tracing JIT compiler.
We also welcome developers of other dynamic languages to see what RPython can do for them.
This release supports:

What else is new?

There are many incremental improvements to RPython and PyPy, the complete listing is here.

Please update, and continue to help us make PyPy better.

Cheers, The PyPy team

[0] We skipped 5.5 since we share a code base with PyPy3, and PyPy3.3-v.5.5-alpha was released last month

We have released PyPy2.7 v5.6 [0], about two months after PyPy2.7 v5.4. This new PyPy2.7 release includes the upstream stdlib version 2.7.12.

We continue to make incremental improvements to our C-API compatibility layer (cpyext). We pass all but 12 of the over-6000 tests in the upstream NumPy test suite, and have begun examining what it would take to support Pandas and PyQt.

Work proceeds at a good pace on the PyPy3.5 version due to a grant from the Mozilla Foundation, and some of those changes have been backported to PyPy2.7 where relevant.

The PowerPC and s390x backend have been enhanced with the capability to use SIMD instructions for micronumpy loops.

We changed timeitto now report average +/- standard deviation, which is better than the misleading minimum value reported in CPython.

We now support building PyPy with OpenSSL 1.1 in our built-in _ssl module, as well as maintaining support for previous versions.

CFFI has been updated to 1.9, improving an already great package for interfacing with C.

As always, this release fixed many issues and bugs raised by the growing community of PyPy users. We strongly recommend updating. You can download the PyPy2.7 v5.6 release here:

Downstream packagers have been hard at work. The Debian package is already available, and the portable PyPy versions are also ready, for those who wish to run PyPy on other Linux distributions like RHEL/Centos 5.

We would like to thank our donors for the continued support of the PyPy project.

We would also like to thank our contributors and encourage new people to join the project. PyPy has many layers and we need help with all of them: PyPy and RPython documentation improvements, tweaking popular modules to run on pypy, or general help with making RPython’s JIT even better.

What is PyPy?

PyPy is a very compliant Python interpreter, almost a drop-in replacement for CPython 2.7. It’s fast (PyPy and CPython 2.7.x performance comparison) due to its integrated tracing JIT compiler.
We also welcome developers of other dynamic languages to see what RPython can do for them.
This release supports:

Thursday, November 3, 2016

We are happy to announce that JIT support in both the PowerPC backend and the
s390x backend have been enhanced. Both can now vectorize loops via SIMD
instructions. Special thanks to IBM for funding this work.

If you are not familiar with this topic you can read more details here.

There are many more enhancements under the hood. Most notably, all pure operations are now delayed until the latest possible point. In some cases indices have been calculated more than once or they needed an additional register, because the old value is still used. Additionally it is now possible to load quadword-aligned memory in both PPC and s390x (x86 currently cannot do that).

NumPy & CPyExt

The community and core developers have been moving CPyExt towards a complete, but emulated, layer for CPython C extensions. This is great, because the one restriction preventing the wider deployment of PyPy in several scenarios will hopefully be removed. However, we advocate not to use CPyExt, but rather to not write C code at all (let PyPy speed up your Python code) or use cffi.

The work done here to support vectorization helps micronumpy (NumPyPy) to speed up operations for PPC and s390x. So why is PyPy supporting both NumPyPy and NumPy, do we actually need both? Yes, there are places where gcc can beat the JIT, and places where the tight integration between NumPyPy and PyPy is more performant. We do have plans to integrate both, hijacking the C-extension method calls to use NumPyPy where we know NumPyPy can be faster.

Just to give you an idea why this is a benefit:

NumPy arrays can carry custom dtypes and apply user defined python functions on the arrays. How could one optimize this kind of scenario? In a traditional setup, you cannot. But as soon as NumPyPy is turned on, you can suddenly JIT compile this code and vectorize it.

Another example is element access that occurs frequently, or any other calls that cross between Python and the C level frequently.

Benchmarks

Let's have a look at some benchmarks reusing mikefc's numpy benchmark suite (find the forked version here). I only ran a subset of microbenchmarks, showing that the core functionality isfunctioning properly. Additionally it has been rewritten to use perf instead of the timeit stdlib module.

Setup

x86 runs on a Intel i7-2600 clocked at 3.40GHz using 4 cores. PowerPC runs on the Power 8 clocked at 3.425GHz providing 160 cores. Last but not least the mainframe machine clocked up to 4 GHz, but fully virtualized (as it is common for such machines). Note that PowerPC is a non private remote machine. It is used by many users and it is crowded with processes. It is hard to extract a stable benchmark there.

x86 ran on Fedora 24 (kernel version of 4.8.4), PPC ran on Fedora 21 (kernel version 3.17.4) and s390x ran on Redhat Linux 7.2 (kernel version 3.10.0). Respectivley, numpy on cpython had openblas available on x86, no blas implementation were present on s390x and PPC provided blas and lapack.

As you can see all machines run very different configurations. It does not make sense to compare across platforms, but rather implementations on the same platform.

Blue shows CPython 2.7.10+ available on that platform using the latest NumPy (1.11). Micro NumPy is used for PyPy. PyPy+ indicates that the vectorization optimization is turned on.
All bar charts show the median value of all runs (5 samples, 100 loops, 10 inner loops, for the operations on vectors (not matrices) the loops are set to 1000). PyPy additionally gets 3 extra executions to warmup the JIT.

The comparison is really comparing speed of machine code. It compares the PyPy's JIT output vs GCC's output. It has little to do with the speed of the interpreter.

Both new SIMD backends speedup the numeric kernels. Some times it is near to the speed of CPython, some times it is faster. The maximum parallelism very much depends on the extension emitted by the compiler. All three SIMD backends have the same vector register size (which is 128 bit). This means that all three behave similar but ppc and s390x gain more because they can load 128bit of memory from quadword aligned memory.

Future directions

Python is achieving rapid adoption in data science. This is currently a trend emerging in Europe, and Python is already heavily used for data science in the USA many other places around the world.

PyPy can make a valuable contribution for data scientists, helping them to rapidly write scientific programs in Python and run them at near native speed. If you happen to be in that situation, we are eager to hear you feedback or resolve your issues and also work together to improve the performance of your,
code. Just get in touch!

Richard Plangger (plan_rich) and the PyPy team

We are happy to announce that JIT support in both the PowerPC backend and the
s390x backend have been enhanced. Both can now vectorize loops via SIMD
instructions. Special thanks to IBM for funding this work.

If you are not familiar with this topic you can read more details here.

There are many more enhancements under the hood. Most notably, all pure operations are now delayed until the latest possible point. In some cases indices have been calculated more than once or they needed an additional register, because the old value is still used. Additionally it is now possible to load quadword-aligned memory in both PPC and s390x (x86 currently cannot do that).

NumPy & CPyExt

The community and core developers have been moving CPyExt towards a complete, but emulated, layer for CPython C extensions. This is great, because the one restriction preventing the wider deployment of PyPy in several scenarios will hopefully be removed. However, we advocate not to use CPyExt, but rather to not write C code at all (let PyPy speed up your Python code) or use cffi.

The work done here to support vectorization helps micronumpy (NumPyPy) to speed up operations for PPC and s390x. So why is PyPy supporting both NumPyPy and NumPy, do we actually need both? Yes, there are places where gcc can beat the JIT, and places where the tight integration between NumPyPy and PyPy is more performant. We do have plans to integrate both, hijacking the C-extension method calls to use NumPyPy where we know NumPyPy can be faster.

Just to give you an idea why this is a benefit:

NumPy arrays can carry custom dtypes and apply user defined python functions on the arrays. How could one optimize this kind of scenario? In a traditional setup, you cannot. But as soon as NumPyPy is turned on, you can suddenly JIT compile this code and vectorize it.

Another example is element access that occurs frequently, or any other calls that cross between Python and the C level frequently.

Benchmarks

Let's have a look at some benchmarks reusing mikefc's numpy benchmark suite (find the forked version here). I only ran a subset of microbenchmarks, showing that the core functionality isfunctioning properly. Additionally it has been rewritten to use perf instead of the timeit stdlib module.

Setup

x86 runs on a Intel i7-2600 clocked at 3.40GHz using 4 cores. PowerPC runs on the Power 8 clocked at 3.425GHz providing 160 cores. Last but not least the mainframe machine clocked up to 4 GHz, but fully virtualized (as it is common for such machines). Note that PowerPC is a non private remote machine. It is used by many users and it is crowded with processes. It is hard to extract a stable benchmark there.

x86 ran on Fedora 24 (kernel version of 4.8.4), PPC ran on Fedora 21 (kernel version 3.17.4) and s390x ran on Redhat Linux 7.2 (kernel version 3.10.0). Respectivley, numpy on cpython had openblas available on x86, no blas implementation were present on s390x and PPC provided blas and lapack.

As you can see all machines run very different configurations. It does not make sense to compare across platforms, but rather implementations on the same platform.

Blue shows CPython 2.7.10+ available on that platform using the latest NumPy (1.11). Micro NumPy is used for PyPy. PyPy+ indicates that the vectorization optimization is turned on.
All bar charts show the median value of all runs (5 samples, 100 loops, 10 inner loops, for the operations on vectors (not matrices) the loops are set to 1000). PyPy additionally gets 3 extra executions to warmup the JIT.

The comparison is really comparing speed of machine code. It compares the PyPy's JIT output vs GCC's output. It has little to do with the speed of the interpreter.

Both new SIMD backends speedup the numeric kernels. Some times it is near to the speed of CPython, some times it is faster. The maximum parallelism very much depends on the extension emitted by the compiler. All three SIMD backends have the same vector register size (which is 128 bit). This means that all three behave similar but ppc and s390x gain more because they can load 128bit of memory from quadword aligned memory.

Future directions

Python is achieving rapid adoption in data science. This is currently a trend emerging in Europe, and Python is already heavily used for data science in the USA many other places around the world.

PyPy can make a valuable contribution for data scientists, helping them to rapidly write scientific programs in Python and run them at near native speed. If you happen to be in that situation, we are eager to hear you feedback or resolve your issues and also work together to improve the performance of your,
code. Just get in touch!